Many signals derived from real world systems exhibit anomalous behaviors such as sudden transients in the form of spikes or dips. It is often desirable to detect these anomalies in signals so that the anomalies may be characterized. In particular, statistics based anomaly detection algorithms determine a range based on estimated statistics of the signal. For example, the range may correspond to a number of standard deviations away from a mean, and samples outside the range may be identified as anomalies. Statistics based anomaly detection may offer several advantages, such being robust (the detection is not as susceptible to noise as other methods), and having low false alarm rates. However, statistics based anomaly detection also has several disadvantages. In particular, these algorithms compress a large amount of information found in a distribution into a single range. By using a range to characterize a distribution, these algorithms lose a lot of information in the distribution, such as the overall shape of the distribution. Importantly, the performance of these algorithms is especially poor when the distribution is not heavily weighted at a centroid.
Systems and methods to detect anomalies would therefore be of great benefit in data analysis.